What characterizes a normal distribution?

A normal distribution is characterized by the fact that most scores fall near the mean, creating a symmetrical bell curve. In a normal distribution, the mean, median, and mode are all located at the center of the curve, and as you move away from the center, the frequency of scores decreases symmetrically. This shape indicates that the majority of observations cluster around the average, with fewer observations occurring as you move toward the extremes.

The bell curve is an essential feature of the normal distribution as it demonstrates the predictable pattern of variability found in many natural phenomena, like heights or test scores. This understanding of the distribution is fundamental in statistics, as it allows researchers to apply various statistical methods and inferential techniques, assuming the data follows this pattern.

Other options do present aspects related to distributions, but they do not fully capture the essence of what constitutes a normal distribution. For instance, scores being evenly distributed suggests a uniform distribution rather than a normal distribution, while concentrating on the highest scores in the middle could imply a skewed distribution rather than reflecting the bell-shaped characteristic. Finally, stating that scores have no regular pattern aligns more with randomness or chaos rather than the structured shape of a normal distribution.